Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/131366
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Type: Conference paper
Title: Gossip learning of personalized models for vehicle trajectory prediction
Author: Dinani, M.A.
Holzer, A.
Nguyen, H.
Marsan, M.A.
Rizzo, G.
Citation: Proceedings of the IEEE Wireless Communications and Networking Conference Workshops (WCNCW 2021), 2021, pp.1-7
Publisher: IEEE
Issue Date: 2021
ISBN: 9781728195070
Conference Name: IEEE Wireless Communications and Networking Conference Workshops (WCNCW) (29 Mar 2021 - 29 Mar 2021 : virtual online)
Statement of
Responsibility: 
Mina Aghaei Dinani, Adrian Holzer, Hung Nguyen, Marco Ajmone Marsan, Gianluca Rizzo
Abstract: Gossip Learning (GL) is a peer-to-peer machine learning protocol based on direct, opportunistic exchange of models among nodes via wireless D2D communications, and on collaborative model training, which has recently proven to scale efficiently to large numbers of nodes, and to offer better privacy guarantees than traditional centralized learning architectures. Existing approaches to GL are however limited to scenarios in which nodes are static, or in which the node connectivity graph is fully connected, and they are fragile to node churn as well as to any change in network configuration. To overcome this limitation, we present a new decentralized architecture for GL suitable for setups with dynamic nodes, which benefits from node mobility instead of being hampered by it. In our approach, nodes improve their personalized model instance by sharing it with neighbors, and by weighting neighbors' contributions according to an estimate of their marginal utility. We apply our GL algorithm to short-term vehicular trajectory estimation in realistic urban scenarios. We propose a new strategy for the estimation of the neighbors' instances marginal utility, which yields satisfactory trajectory estimation accuracy for nodes with long enough sojourn times.
Rights: © 2021 IEEE.
DOI: 10.1109/WCNCW49093.2021.9420038
Published version: https://ieeexplore.ieee.org/xpl/conhome/9419968/proceeding
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Computer Science publications

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